en_Evaluation Metrics in Regression Models.txt

Hello, and welcome!
In this video, we’ll be covering accuracy metrics for model evaluation.
So, let’s get started.
Evaluation metrics are used to explain the performance of a model.
Let’s talk more about the model evaluation metrics that are used for regression.
As mentioned, basically, we can compare the actual values and predicted values to calculate
the accuracy of a regression model.
Evaluation metrics provide a key role in the development of a model, as it provides insight
to areas that require improvement.
We’ll be reviewing a number of model evaluation metrics, including:
Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE).
But, before we get into defining these, we need to define what an error actually is.
In the context of regression, the error of the model is the difference between the data
points and the trend line generated by the algorithm.
Since there are multiple data points, an error can be determined in multiple ways.
Mean absolute error is the mean of the absolute value of the errors.
This is the easiest of the metrics to understand, since it’s just the average error.
Mean Squared Error (MSE) is the mean of the squared error.
It’s more popular than Mean absolute error because the focus is geared more towards large
errors.
This is due to the squared term exponentially increasing larger errors in comparison to
smaller ones.
Root Mean Squared Error (RMSE) is the square
root of the mean squared error.
This is one of the most popular of the evaluation metrics because Root Mean Squared Error is
interpretable in the same units as the response vector (or ‘y’ units) making it easy to relate
its information.
Relative Absolute Error (RAE), also known as Residual sum of square, where y-bar is a mean value
of y, takes the total absolute error and normalizes it by dividing by the total absolute error
of the simple predictor.
Relative Squared Error (RSE) is very similar to “Relative absolute error “, but is widely
adopted by the data science community, as it is used for calculating R-squared.
R-squared is not error, per se, but is a popular metric for the accuracy of your model.
It represents how close the data values are to the fitted regression line.
The higher the R-squared, the better the model fits your data.
Each of these metrics can be used for quantifying of your prediction.
The choice of metric completely depends on the type of model, your data type, and domain
of knowledge.
Unfortunately, further review is out of scope of this course.
Thanks for watching!